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End-to-end Provenance Traceability and Reproducibility Through "Palletized'' Simulation Data

Lofstead, Gerald F.; Younge, Andrew J.; Baker, Joshua

Trusting simulation output is crucial for Sandia's mission objectives. We rely on these simulations to perform our high-consequence mission tasks given our treaty obligations. Other science and modelling needs, while they may not be high-consequence, still require the strongest levels of trust to enable using the result as the foundation for both practical applications and future research. To this end, the computing community has developed work- flow and provenance systems to aid in both automating simulation and modelling execution, but to also aid in determining exactly how was some output created so that conclusions can be drawn from the data. Current approaches for workflows and provenance systems are all at the user level and have little to no system level support making them fragile, difficult to use, and incomplete solutions. The introduction of container technology is a first step towards encapsulating and tracking artifacts used in creating data and resulting insights, but their current implementation is focused solely on making it easy to deploy an application in an isolated "sandbox" and maintaining a strictly read-only mode to avoid any potential changes to the application. All storage activities are still using the system-level shared storage. This project was an initial exploration into extending the container concept to also include storage and to use writable containers, auto generated by the system, as a way to link the contained data back to the simulation and input deck used to create it.

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TYPE SAND Report YEAR 2018
DOIOSTI

Quiho: Automated performance regression testing using inferred resource utilization profiles

ICPE 2018 - Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering

Jimenez, Ivo; Watkins, Noah; Sevilla, Michael; Lofstead, Gerald F.; Maltzahn, Carlos

We introduce quiho, a framework for profiling application performance that can be used in automated performance regression tests. quiho profiles an application by applying sensitivity analysis, in particular statistical regression analysis (SRA), using application-independent performance feature vectors that characterize the performance of machines. The result of the SRA, feature importance specifically, is used as a proxy to identify hardware and low-level system software behavior. The relative importance of these features serve as a performance profile of an application (termed inferred resource utilization profile or IRUP), which is used to automatically validate performance behavior across multiple revisions of an application's code base without having to instrument code or obtain performance counters. We demonstrate that quiho can successfully discover performance regressions by showing its effectiveness in profiling application performance for synthetically introduced regressions as well as those found in real-world applications.

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TYPE Conference Poster YEAR 2018
DOIOSTIScopus

Predicting output performance of a petascale supercomputer

HPDC 2017 - Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing

Xie, Bing; Huang, Yezhou; Chase, Jefrey S.; Choi, Jong Y.; Klasky, Scott; Lofstead, Gerald F.; Oral, Sarp

In this paper, we develop a predictive model useful for output performance prediction of supercomputer file systems under production load. Our target environment is Titan-the 3rd fastest supercomputer in the world-and its Lustre-based multi-stage write path. We observe from Titan that although output performance is highly variable at small time scales, the mean performance is stable and consistent over typical application run times. Moreover, we find that output performance is non-linearly related to its correlated parameters due to interference and saturation on individual stages on the path. These observations enable us to build a predictive model of expected write times of output patterns and I/O configurations, using feature transformations to capture non-linear relationships. We identify the candidate features based on the structure of the Lustre/Titan write path, and use feature transformation functions to produce a model space with 135,000 candidate models. By searching for the minimal mean square error in this space we identify a good model and show that it is effective.

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TYPE Conference Poster YEAR 2017
DOIOSTIScopus
Results 51–75 of 122
Results 51–75 of 122